A Web-Based Decision Support System for Chronic Diseases

Individuals vary in survival chances due to differences in genetics, environmental exposures, and gene-environment interactions. These chances, as well as the contribution of each factor to mortality, change as individuals get older. In general, human physiological systems are constructed by collecting more than one part to perform either single or multiple functions. In addition, the successive times between failures are not necessarily identically distributed. More generally, they can become smaller (an indication of deterioration). However, if any critical deterioration is detected, then the decision of when to take the intervention, given the costs of diagnosis and therapeutics, is of fundamental importance. At the time of the decision, the degree of future physiological system deterioration, which is likely to be uncertain, is of primary interest for the decision maker. This paper develops a possible Webbased decision support system by considering the sensitivity analysis as well as the optimal prior and posterior decisions for aging chronic diseases. The proposed design of Bayesian decision support systems facilitates the effective use of the computing capability of computers and provides a systematic way to integrate the expert’s opinions and the sampling information which will furnish decision makers with valuable support for quality decision making.